The quality of state-of-the-art products is becoming increasingly important as these products become a fundamental part of our modern, high tech economy. Manufacturers continue to focus on quality control and reproducibility to meet the demands of the high tech economy. Process control is used to produce the most consistent product properties in a manufacturing process. Quality control is essential in production lines where intricate or otherwise information-sensitive manufacturing is performed.
Poor quality control can drastically affect manufactured products. Improper or deficient process control can result in a product that is of reduced value or even useless to a user. Manufacturers are negatively impacted by poor process control because a manufacturer may pay manufacturing costs for a useless product, lose the opportunity to make a profit on an acceptable product, or lose revenue from a noncompliant product's reduced selling price. Thus, process control can affect whether the manufacturer's business survives or fails.
Manufacturing environments require measurement systems that have low overall total measurement uncertainty (TMU). Measurement uncertainty characterizes the dispersion of values attributed to a measured quantity. TMU can depend on the precision of the tool being used to take the measurements, as well as the tool-to-tool matching of the fleet of possible measurement tools.
In complicated manufacturing environments, many variables are in flux simultaneously. While a semiconductor environment is referred to herein, the principles are general to any manufacturing environment. For example, in a semiconductor manufacturing environment, variables like the process recipe, measurement tool recipe, overall process health, measurement tool health, and other parameters are all in flux. Providing a technique to monitor the measurement variation in a manufacturing facility to ensure variation is not getting worse over time can be valuable to a manufacturer. If a shift is detected, there can be a fast response to address the detected shift.
There are several existing methods to address the concerns of excessive measurement variation in a semiconductor manufacturing environment. However, semiconductor manufacturers and tool manufacturers are typically not in agreement with respect to optimal measurement system monitoring methods. Semiconductor manufacturers can use monitoring methods that meet manufacturing requirements for a measurement system and typically measure health of a measurement system using naturally-occurring production data. For example, all production manufacturing data can be used. Production wafer data is generally used instead of reference wafer data. Matching results can be measured continuously. Semiconductor manufacturer techniques can include boxplots, simple means and standard deviations from production, statistical test of production (e.g., analysis of variance (ANOVA), ttest), or statistical test of certain “golden wafers” or reference wafers (e.g., ANOVA, ttest). In contrast, tool manufacturers (which sell and/or service the equipment being used by the semiconductor manufacturer) tend to focus on clean experiments to separate measurement from process variation. These experiments may not generate increasing alarm percentages with production volume. The percent of matching-related alarms may not increase as precision improves, and vice-versa. Tool manufacturer techniques can include matching studies and a daily monitor of reference wafers against specifications. Thus, the monitoring methods of semiconductor manufacturers and tool manufacturers are not aligned.
Therefore, what is needed are improved process control techniques.